1 Terms of re-use

1.1 License

CC-BY-SA unless otherwise noted.

1.2 Citation

2 Purpose

I’ve not been in NZ at this time of year before so #birdoftheyear OR #boty is a whole new cultural experience.

The idea is to extract and visualise tweets and re-tweets of #birdoftheyear OR #boty (see https://twitter.com/hashtag/birdoftheyear and the Forest & Bird voting site).

Why? Err…. Just. Because.

3 How it works

Code borrows extensively from https://github.com/mkearney/rtweet

The analysis used rtweet to ask the Twitter search API to extract ‘all’ tweets containing the #birdoftheyear OR #boty hashtags in the ‘recent’ twitterVerse.

It is therefore possible that not quite all tweets have been extracted although it seems likely that we have captured most recent human tweeting which was the main intention. Future work should instead use the Twitter streaming API.

## [1] "Found 30 files matching #birdoftheyear OR #boty in ~/Data/twitter/"

The data has:

4 Analysis

4.1 Tweets and Tweeters over time

Number of tweets and tweeters

Figure 4.1: Number of tweets and tweeters

Figure 4.1 shows the number of tweets and tweeters in the data extract by day. The quotes, tweets and re-tweets have been separated.

If you are in New Zealand and you are wondering why there are no tweets today (2018-10-05) the answer is that twitter data (and these plots) are working in UTC and (y)our today() may not have started yet in UTC. Don’t worry, all the tweets are here - it’s just our old friend the timezone… :-)

4.2 Who’s tweeting?

Next we’ll try by screen name.

N tweets per day by screen name

Figure 4.2: N tweets per day by screen name

Figure 4.2 is a really bad visualisation of all tweeters tweeting over time. Each row of pixels is a tweeter (the names are probably illegible) and a green dot indicates a few tweets in the given day while a red dot indicates a lot of tweets.

So let’s re-do that for the top 50 tweeters so we can see their tweetStreaks (tm)…

Top tweeters:

Table 4.1: Top 15 tweeters (all days)
screen_name nTweets
birdoftheyear 282
Forest_and_Bird 112
testeeves 109
vote4kaki 88
NatForsdick 85
coolbiRdpics 71
mifflangstone 63
freshwaterfelix 54
jackcraw57 51
hugobrown 50
64by4 47
thebushline 42
newzealandbirds 41
kiwilullaby 41
kimi_collins 36

And their tweetStreaks are shown in Figure 4.3…

N tweets per day minutes by screen name (top 50, reverse alphabetical)

Figure 4.3: N tweets per day minutes by screen name (top 50, reverse alphabetical)

Any twitterBots…?

4.3 Which birds are mentioned the most (by hashtag)

This is very quick and dirty but… to calculate this we have to do a bit of string processing first.

This is how I have tidied the hashtags (make other suggestions here):

# First we make everything lower case
htLongDT <- htLongDT[, htLower := tolower(htOrig)] # lower case

# Next we remove the macrons so that TeamKakī == TeamKaki & takahe == takahē etc 
# h/t: https://twitter.com/Thoughtfulnz/status/1046685305569345536
htLongDT <- htLongDT[, htClean := stringr::str_replace_all(htLower,"[āēīōū]",hashTagR::deMacron)]

# Now remove 'team' from a string so that e.g. teamkaki == kaki
htLongDT <- htLongDT[, htClean := gsub("team", "",htClean)]

# Now remove variants on 'vote'
htLongDT <- htLongDT[, htClean := gsub("vote4", "",htClean)]
htLongDT <- htLongDT[, htClean := gsub("vote", "",htClean)]

Table 4.2 shows the total count of each #hashtag by (re)tweet type. With thanks to David Hood for code to help make sure that kakī == kaki (etc).

Table 4.2: Top 20 hashtags in tweets containing #birdoftheyear or #boty
hashTag type count
birdoftheyear Re-tweet 2983
birdoftheyear Tweet 1818
takayay Re-tweet 511
birdoftheyear Quote 494
kaki Re-tweet 453
boty Re-tweet 163
dammitgannet Re-tweet 149
kereru Re-tweet 149
boty Tweet 130
kaki Tweet 130
ruru Re-tweet 108
dammitgannet Tweet 91
rockhopper Tweet 88
rockhopper Re-tweet 81
takahe Re-tweet 78
kaki Quote 72
aotearoa Re-tweet 65
hihi Re-tweet 63
greatkererucount Re-tweet 62
nativebird Re-tweet 62

Figure 4.4 plots the daily occurence of these hashtags after removing variants of #birdOfTheYear and #boty and selecting only those which have more than 10 mentions on any day. For clarity tweets and re-tweets are aggregated. See Section 6 for the problems with this #hashTag counting approach.

Most mentioned #hashtags per day

Figure 4.4: Most mentioned #hashtags per day

5 So who’s gonna win?

No idea.

There are a lot of problems with this approach (see Section 6) but if the hashtags have any predictive value at all then Figure 5.1 should be an indicator of the direction of travel (watch for lines of apparently dis-similar hashtags where the macron fix has failed) and Figure 5.2 shows the totals to date.

Figure 5.1 uses plotly to avoid having to render a large legend - just hover over the lines to see who is who…

Figure 5.1: Cumulative hashtag counts over time (only total count > 30 shown)

Total hashtag counts to date (only total count > 30 shown)

Figure 5.2: Total hashtag counts to date (only total count > 30 shown)

6 Problems

Loads of them. But primarily:

7 About

Analysis completed in 50.292 seconds ( 0.84 minutes) using knitr in RStudio with R version 3.5.1 (2018-07-02) running on x86_64-apple-darwin15.6.0.

A special mention must go to https://github.com/mkearney/rtweet (Kearney 2018) for the twitter API interaction functions.

Other R packages used:

References

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